Fujitsu

6702

Fujitsu is developing a superconducting quantum computer with RIKEN and offers the Digital Annealer, a quantum-inspired architecture for combinatorial optimization. The company emphasizes hybrid quantum-classical workflows, pairing evolving quantum hardware with classical systems and software for industrial use.

AI-written profile · not yet reviewed · 8 September 2026

Headquarters: Tokyo, Japan
Founded: 1935
Status: Public

superconducting algorithms software

Coverage

HPCwire9 October 2026

Fujitsu and New Mexico to Establish Quantum Computing Research Center

Fujitsu announced a collaboration with New Mexico to establish a quantum computing research center in the state. The center will pursue research across quantum computing hardware, software, algorithms, and theory. It will also support education and workforce-development programs aimed at expanding the quantum talent pipeline.

OutlookPlausible

This could give Fujitsu a US-based superconducting quantum research and talent hub, enabling earlier hardware demonstrations and a trained regional workforce within two years.

superconducting algorithms softwareFujitsuNew Mexico

Quantum Computing Report7 September 2026

NEC Discontinues Superconducting Quantum Computer Development to Focus on Annealing and Classical Emulation

NEC Corporation has ended its research and development of superconducting quantum computers. The company will shift toward quantum-inspired annealing and classical emulation, concentrating on software and optimization services. Fujitsu remains the main Japanese corporate developer of superconducting quantum hardware.

OutlookPlausible

NEC could package its annealing and classical emulation capabilities into commercial optimization services for Japanese enterprises within the next two years.

annealing algorithms software superconductingFujitsuNEC

Source

Chaotic Quantum System Learns Data With Simple Controls (Fujitsu Research)

Fujitsu Research has proposed a quantum generative model that uses chaotic Hamiltonian time evolution as the forward process in a diffusion framework. The aim is to learn distributions over quantum data using only simple control parameters, instead of training deep parameterized quantum circuits. The work is presented as a research proposal, not yet a hardware demonstration.

OutlookPlausible

Small-scale implementations on a handful of qubits could appear within two years, testing whether chaotic Hamiltonian diffusion can learn simple quantum data distributions such as random states or ground states of local Hamiltonians.

algorithms softwareFujitsuGoogleIBMXanadu